Biology subjects

Pavan, C.

Publications and source records attributed to Pavan, C..

3 recordsLinked to original sources

bioRxiv2026

Machine learning-based image analysis of Parkinson's disease iPS-derived neurons predicts genotype and reveals mitochondria-lysosome abnormalities

Mitochondrial and lysosomal dysfunction are central features of Parkinsons disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from PD patients and healthy controls (13 lines total). Supervised machine-learning models distinguished PD neurons from controls with high accuracy (AUC = 0.87) and reliably separated individual genotypes. Feature importance and attribution analysis revealed genotype-specific organelle biases, with mitochondrial features dominating classification in PRKN neurons, balanced mitochondrial and lysosomal contributions in SNCA neurons, and a greater lysosomal contribution in LRRK2 neurons. Multi-class models retained strong performance, and findings were reproduced across two independent laboratories using different dyes and imaging conditions. These results demonstrate that morphomics provides a robust and scalable framework to quantify genotype-specific organelle abnormalities in PD neurons and supports its application for cellular stratification and biomarker discovery.

neuroscience↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2024

Dopamine and cortical iPSC-derived neurons with different Parkinsonian mutations show variation in lysosomal and mitochondrial dysfunction: implications for protein deposition versus selective cell loss

BackgroundMutations causing Parkinsons disease (PD) give diverse pathological phenotypes whose cellular correlates remain to be determined. For example, those with PRKN loss of function mutations have significantly earlier selective vulnerability of dopamine neurons, those with SNCA mutations have increased alpha-synuclein deposition, while those with LRRK2 mutations have additional deposition of tau. Yet all three mutation types are implicated in mitochondrial and/or lysosomal dysfunction. Direct comparison of cell models with these mutations would clarify the relative cellular dysfunctions associated with these different pathological phenotypes. MethodsAn unbiased high-content imaging platform using orthogonal probes to assess both lysosomal and mitochondrial dysfunction, along with alpha-synuclein and tau protein deposition was established using induced pluripotent stem cell (iPSC) derived cortical and ventral midbrain neurons. Three mutation types, SNCA A53T, LRRK2 R1441G and PRKN loss of function (lof), were selected as exemplars of divergent PD pathological phenotypes and compared to each other, and to control iPSC from subjects without PD. ResultsDifferent PD mutations caused cell type specific dysfunctions, likely to impact on both selective neuronal vulnerability and the pathologies observed in PD. Comparison of dopamine neurons identified that both lysosomal and mitochondrial dysfunction were predominant with PRKN lof mutations, whereas immunofluorescent staining revealed that SNCA A53T and LRRK2 R1441G mutations had increased tau deposition. In contrast, cortical neurons with SNCA and LRRK2 mutations both had mitochondrial and autophagy impairments without protein deposition, with LRRK2 cells additionally showing decreased glucocerebrosidase activity and increased alpha-synuclein phosphorylation. ConclusionsLysosomal and mitochondrial dysfunction are predominant in dopamine neurons with PRKN lof mutations, and may drive the early selective loss of dopamine neurons in PRKN mutation carriers. More subtle cellular abnormalities in the SNCA A53T cell lines are likely to predispose to alpha-synuclein aggregation and tau protein deposition over time. The LRRK2 R1441G may also predispose to tau deposition, but despite substantial lysosomal dysfunction with increased alpha-synuclein phosphorylation, pathological alpha-synuclein accumulations were not observed. Understanding the mechanistic differences in how lysosomal and mitochondrial dysfunction impact on PD pathogenesis in different disease subtypes may be important for therapeutic development.

neuroscience↗